# resample with replacement each row
boot_slopes = []
boot_interc = []
n_boots = 100
plt.figure()
for _ in range(n_boots):
 # sample the rows, same size, with replacement
 sample_df = data_df.sample(n=n_points, replace=True)
 # fit a linear regression
 ols_model_temp = sm.ols(formula = 'y ~ x', data=sample_df)
 results_temp = ols_model_temp.fit()

 # append coefficients
 boot_interc.append(results_temp.params[0])
 boot_slopes.append(results_temp.params[1])

 # plot a greyed out line
 y_pred_temp = ols_model_temp.fit().predict(sample_df['x'])
 plt.plot(sample_df['x'], y_pred_temp, color='grey', alpha=0.2)